
Internal validity of the effect estimates derived from the index and competitor studies. This is certainly feasible where the studies are RCTs because randomization ensures exchangeability over treatment assignment on expectation. While internal validity may hold in RCTs, it is a more stringent condition for observational studies. The absence of informative measurement error, missing data, non-adherence, etc. is assumed.

Consistency under parallel studies [1]}. There is only one well-defined version of each treatment [2]} or any variations in the versions of treatment are irrelevant [3]}, [4]}. This applies to the common comparator \(C\)  in particular.

Conditional transportability (exchangeability) of the marginal treatment effect for \(A\)  vs. \(C\)  from the index to the competitor study [5]}. Namely, trial assignment does not affect this measure, conditional on \(\mathbf {z}\) . Prior research has referred to this assumption as the conditional constancy of relative effects [6]}, [7]}, [8]}. It is plausible if \(\mathbf {z}\)  comprises all of the covariates that are considered to modify the marginal treatment effect for \(A\)  vs. \(C\)  (i.e., there are no unmeasured effect modifiers) [9]}, [10]}, [11]}.aThis assumption is strong and untestable. Nevertheless, it is weaker than that required by unanchored comparisons. Unanchored comparisons compare absolute outcome means as opposed to relative effect estimates. Therefore, these rely on the conditional exchangeability of the absolute outcome mean under active treatment (conditional constancy of absolute effects) [6]}, [7]}, [14]}, [15]}. This requires capturing all factors that are prognostic of outcome given active treatment.


Sufficient overlap. The ranges of the selected covariates in \(S=1\)  should cover their respective moments in \(S=2\) . Overlap violations can be deterministic or random. The former arise structurally, due to non-overlapping trial target populations (eligibility criteria). The latter arise empirically due to chance, particularly where sample sizes are small [16]}. Therefore, overlap can be assessed based on absolute sample sizes. The ESS is a convenient one-number diagnostic.

Correct specification of the \(S=2\)  covariate distribution. Analysts can only approximate the joint distribution because IPD are unavailable for the competitor study. Covariate correlations are rarely published for \(S=2\)  and therefore cannot be balanced by MAIC. In that case, they are assumed equal to those in the pseudo-sample formed by weighting the IPD [6]}.

